Cover
Vol. 19 No. 2 (2023)

Published: December 31, 2023

Pages: 90-99

Original Article

An Efficient Path Planning in Uncertainty Environments using Dynamic Grid-Based and Potential Field Methods

Suhaib Al-Ansarry 1 Salah Al-Darraji Dhafer G. Honi
1Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq
History
  • Received: March 22, 2023
  • Revised: April 29, 2023
  • Accepted: May 1, 2023
  • Available Online: July 7, 2023
DOI

https://doi.org/10.37917/ijeee.19.2.11

Abstract

Path planning is an essential concern in robotic systems, and it refers to the process of determining a safe and optimal path starting from the source state to the goal one within dynamic environments. We proposed an improved path planning method in this article, which merges the Dijkstra algorithm for path planning with Potential Field (PF) collision avoidance. In real-time, the method attempts to produce multiple paths as well as determine the suitable path that’s both short and reliable (safe). The Dijkstra method is employed to produce multiple paths, whereas the Potential Field is utilized to assess the safety of each route and choose the best one. The proposed method creates links between the routes, enabling the robot to swap between them if it discovers a dynamic obstacle on its current route. Relating to path length and safety, the simulation results illustrate that Dynamic Dijkstra-Potential Field (Dynamic D-PF) achieves better performance than the Dijkstra and Potential Field each separately, and going to make it a promising solution for the application of robotic automation within dynamic environments.

Keywords

References

  1. M. Castillo-Lopez, P. Ludivig, S. A. Sajadi-Alamdari, J. L. Sanchez-Lopez, M. A. Olivares-Mendez, and H. Voos, “A real-time approach for chance-constrained motion planning with dynamic obstacles,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3620– 3625, 2020.
  2. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, 1968. 99 | Al-Ansarry, Al-Darraji & Honi
  3. D. B. Johnson, “A note on dijkstra’s shortest path al- gorithm,” Journal of the ACM (JACM), vol. 20, no. 3, pp. 385–388, 1973.
  4. S. LaValle, “Rapidly-exploring random trees: A new tool for path planning,” Research Report 9811, 1998.
  5. S. Al-Ansarry and S. Al-Darraji, “Hybrid rrt-a*: An improved path planning method for an autonomous mo- bile robots.,” Iraqi Journal for Electrical & Electronic Engineering, vol. 17, no. 1, 2021.
  6. J. Barraquand, B. Langlois, and J.-C. Latombe, “Numer- ical potential field techniques for robot path planning,” IEEE transactions on systems, man, and cybernetics, vol. 22, no. 2, pp. 224–241, 1992.
  7. L. Li-sang, L. Jia-feng, Y. Jin-xin, H. Dong-wei, Z. Ji- shi, J. Huang, and P. Shi, “Path planning for smart car based on dijkstra algorithm and dynamic window ap- proach,” Wireless Communications & Mobile Comput- ing (Online), vol. 2021, 2021.
  8. S. J. Fusic, P. Ramkumar, and K. Hariharan, “Path plan- ning of robot using modified dijkstra algorithm,” in 2018 National Power Engineering Conference (NPEC), pp. 1– 5, IEEE, 2018.
  9. D. S. Nair and P. Supriya, “Comparison of temporal difference learning algorithm and dijkstra’s algorithm for robotic path planning,” in 2018 Second International Conference on Intelligent Computing and Control Sys- tems (ICICCS), pp. 1619–1624, IEEE, 2018.
  10. S. M. Ahmadi, H. Kebriaei, and H. Moradi, “Con- strained coverage path planning: evolutionary and clas- sical approaches,” Robotica, vol. 36, no. 6, pp. 904–924, 2018.
  11. X. Li, “Path planning of intelligent mobile robot based on dijkstra algorithm,” in Journal of Physics: Confer- ence Series, vol. 2083, p. 042034, IOP Publishing, 2021.
  12. H. I. Kang, B. Lee, and K. Kim, “Path planning al- gorithm using the particle swarm optimization and the improved dijkstra algorithm,” in 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, vol. 2, pp. 1002–1004, IEEE, 2008.
  13. Z. Cheng, H. Zhang, and Q. Zhao, “The method based on dijkstra of multi-directional ship’s path planning,” in 2020 Chinese Control And Decision Conference (CCDC), pp. 5142–5146, IEEE, 2020.
  14. R. Szczepanski, T. Tarczewski, and K. Erwinski, “En- ergy efficient local path planning algorithm based on predictive artificial potential field,” IEEE Access, vol. 10, pp. 39729–39742, 2022.
  15. Y. Singh, S. Sharma, R. Sutton, and D. Hatton, “To- wards use of dijkstra algorithm for optimal navigation of an unmanned surface vehicle in a real-time marine environment with results from artificial potential field,” 2018.
  16. B. Kasmi and A. Hassam, “Comparative study between fuzzy logic and interval type-2 fuzzy logic controllers for the trajectory planning of a mobile robot,” Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7011–7017, 2021.
  17. X. Chen, X. Chen, and G. Xu, “The path planning algo- rithm studying about uav attacks multiple moving tar- gets based on voronoi diagram,” International Journal of Control and Automation, vol. 9, no. 1, pp. 281–292, 2016.
  18. W. Yang, P. Wu, X. Zhou, H. Lv, X. Liu, G. Zhang, Z. Hou, and W. Wang, “Improved artificial potential field and dynamic window method for amphibious robot fish path planning,” Applied Sciences, vol. 11, no. 5, p. 2114, 2021.
  19. G. Klanˇcar, A. Zdeˇsar, and M. Krishnan, “Robot navi- gation based on potential field and gradient obtained by bilinear interpolation and a grid-based search,” Sensors, vol. 22, no. 9, p. 3295, 2022.
  20. J. Luo, Z.-X. Wang, and K.-L. Pan, “Reliable path planning algorithm based on improved artificial poten- tial field method,” IEEE Access, vol. 10, pp. 108276– 108284, 2022.
  21. H. Qin, S. Shao, T. Wang, X. Yu, Y. Jiang, and Z. Cao, “Review of autonomous path planning algorithms for mobile robots,” Drones, vol. 7, no. 3, p. 211, 2023.